Currently, enormous volumes of data are being generated by the comprehensive molecular characterization of a number of human tumors. The ability to effectively and efficiently use RNAi to assess the biologic consequences of gene target inhibition is of critical importance to understanding gene function and to uncover tumor-specific vulnerabilities. The identification of tumor-specific vulnerabilities provides rationale for the development of biologically-based targeted therapies. RNAi screening is a powerful technology for high- throughput gene function discovery that has been used to identify tumor-specific vulnerabilities. However there are significant limitations to the RNAi screening resources that are currently available. The RNAi screening tools used to date do not efficiently target the full compendium of cancer relevant genes due to technological limitations in genome coverage and RNAi gene knockdown efficacy. These technological limitations also lead to false-positive and false-negative screen hits. Thus, currently available RNAi screening platforms are not cost-effective for performing high-throughput screens for most labs. Here we present technologies and resources that overcome these limitations, dramatically improving RNAi screening capabilities. We take advantage of statistically-based analyses and the power of new deep sequencing technologies that are being rapidly democratized. Our new approaches will greatly facilitate the development of cancer polytherapies, opening a new paradigm for rationally-based cancer therapeutics that fully capitalize on genomic profiling of human tumors. In order to design effective combination cancer therapies (polytherapies) we must first identify the signaling pathways that act synergistically to promote tumor growth or therapeutic resistance. This knowledge then enables the design of therapies that target these key cancer driver pathways. A major obstacle to the development of therapies that preclude or overcome resistance to targeted cancer therapy is that there is no systematic means by which to identify pathways that functionally cooperate and synergize to drive tumor growth or therapeutic resistance. Therefore, the search for effective cancer polytherapies has been done largely in an ad hoc manner exploring only a very limited number of potential combinations. The key to rationally designing an optimal combination of therapies lies in the systematic identification of pathways that when targeted, lead to specific and synergistic destruction of cancer cells. Our new approaches can determine simultaneously and rapidly (within 1-3 weeks) high precision measures of functional genetic interactions between large numbers (typically 100,000) pairs of shRNAs that target genes of interest in the context of any cancer. This represents a transformative technology in terms of our ability to systematically uncover cancer- relevant gene interaction networks that drive tumor growth and that potentially can be exploited as rational, tumor-specific polytherapies. PUBLIC HEALTH RELEVANCE: As important as the cancer genome sequencing initiatives are, the identification and cataloguing of large numbers of variations is only the first step in efforts to provide a scientific foundation for therapeutic breakthroughs. To achieve this broader goal, we must now understand how these variations alone and critically in combination contribute to the malignant properties of human tumors. Our program aims to fill this void. Our team brings together a critical range of expertise in cancer biology, functional genomics, and systems biology as well as a unique next generation shRNA screening strategy that greatly increases our ability to monitor the precise phenotypic consequences of perturbing combinations of genes. Our ability to distinguish cancer drivers and passengers and identify cancer-relevant signaling networks using our cutting-edge novel gene interaction approach is of high-relevance to the CTD2 mission, and the goal of developing rational combination therapies that may improve outcomes for genetically-defined subsets of cancer patients.